Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests

The Web Conference


Randomized experiments, or “A/B” tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings such as social networks, where users are interacting and influencing one another, violate conventional assumptions of no interference needed for credible causal inference. Existing solutions include accounting for the fraction or count of treated neighbors in a user’s network, among other strategies. Yet, most current methods do not account for the local network structure beyond simply counting the number of neighbors. Capturing local network structures is important because it can account for theories, such as structural diversity and echo chambers. Our study provides an approach that accounts for both the local structure in a user’s social network via motifs as well as the assignment conditions of neighbors. We propose a two-part approach. We first introduce and employ “causal network motifs”, which are network motifs that characterize the assignment conditions in local ego networks; and then we propose a tree-based algorithm for identifying different network interference conditions and estimating their average potential outcomes. We test our method on a real-world experiment on a large-scale network and a synthetic network setting, which highlight how accounting for local structures can better account for different interference patterns in networks.

Related Publications

All Publications

ACM MM - October 20, 2021

EVRNet: Efficient Video Restoration on Edge Devices

Sachin Mehta, Amit Kumar, Fitsum Reda, Varun Nasery, Vikram Mulukutla, Rakesh Ranjan, Vikas Chandra

ICCV - October 11, 2021

Egocentric Pose Estimation from Human Vision Span

Hao Jiang, Vamsi Krishna Ithapu

TSE - June 29, 2021

Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates

Federica Sarro, Rebecca Moussa, Alessio Petrozziello, Mark Harman

Journal of Big Data - July 19, 2021

Cumulative deviation of a subpopulation from the full population

Mark Tygert

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy